Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. This paper introduces a novel Large Language Models (LLMs) based agent framework for resume screening, aimed at enhancing efficiency and time management in recruitment processes. Our framework is distinct in its ability to efficiently summarize and grade each resume from a large dataset. Moreover, it utilizes LLM agents for decision-making. To evaluate our framework, we constructed a dataset from actual resumes and simulated a resume screening process. Subsequently, the outcomes of the simulation experiment were compared and subjected to detailed analysis. The results demonstrate that our automated resume screening framework is 11 times faster than traditional manual methods. Furthermore, by fine-tuning the LLMs, we observed a significant improvement in the F1 score, reaching 87.73\%, during the resume sentence classification phase. In the resume summarization and grading phase, our fine-tuned model surpassed the baseline performance of the GPT-3.5 model. Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingSentenceSentence ClassificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening
Resume screening is a critical yet time-intensive process in talent acquisition, requiring recruiters to analyze vast volume of job applications while remaining objective, accurate, and fair. With the advancements in Lar…
RAGRetrieval-augmented GenerationMLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking
This paper introduces an innovative Applicant Tracking System (ATS) enhanced by a novel Robotic process automation (RPA) framework or as further referred to as MLAR. Traditional recruitment processes often encounter bott…
BenchmarkingLanguage ModelingLanguage ModellingLarge Language ModelSynapse: Evolving Job-Person Fit with Explainable Two-phase Retrieval and LLM-guided Genetic Resume Optimization
Modern recruitment platforms operate under severe information imbalance: job seekers must search over massive, rapidly changing collections of postings, while employers are overwhelmed by high-volume, low-relevance appli…
Contrastive LearningSemantic RetrievalLeveraging Knowledge Graphs for Orphan Entity Allocation in Resume Processing
Significant challenges are posed in talent acquisition and recruitment by processing and analyzing unstructured data, particularly resumes. This research presents a novel approach for orphan entity allocation in resume p…
graph constructionKnowledge Graphsnamed-entity-recognitionNamed Entity RecognitionToward a traceable, explainable, and fairJD/Resume recommendation system
In the last few decades, companies are interested to adopt an online automated recruitment process in an international recruitment environment. The problem is that the recruitment of employees through the manual procedur…